Machine Learning with R Cookbook - Second Edition

How it works...

Logistic regression is very similar to linear regression; the main difference is that the dependent variable in linear regression is continuous but the dependent variable in logistic regression is dichotomous (or nominal). The primary goal of logistic regression is to use logic to yield the probability of a nominal variable being related to the measurement variable. We can formulate logic in the following equation: ln(P/(1-P)), where P is the probability that a certain event occurs.

The advantage of logistic regression is that it is easy to interpret, it directs model logistic probability, and provides a confidence interval for the result. Unlike the decision tree, which makes it difficult to update the model, you can quickly ...